System and methods for joint scan parameter selection

ABSTRACT

Methods and systems are provided for improving image quality of ultrasound images by jointly selecting optimal scan parameter values. In one example, a method includes acquiring a plurality of ultrasound images of an anatomical region, each ultrasound image acquired at a different combination of parameter values for a first scan parameter and a second scan parameter, selecting a first parameter value for the first scan parameter and a second parameter value for the second scan parameter based on an image quality of each image, and acquiring one or more additional ultrasound images at the first parameter value and the second parameter value.

TECHNICAL FIELD

Embodiments of the subject matter disclosed herein relate to ultrasoundimaging, and more particularly, to improving image quality forultrasound imaging.

BACKGROUND

Medical ultrasound is an imaging modality that employs ultrasound wavesto probe the internal structures of a body of a patient and produce acorresponding image. For example, an ultrasound probe comprising aplurality of transducer elements emits ultrasonic pulses which reflector echo, refract, or are absorbed by structures in the body. Theultrasound probe then receives reflected echoes, which are processedinto an image. Ultrasound images of the internal structures may be savedfor later analysis by a clinician to aid in diagnosis and/or displayedon a display device in real time or near real time.

SUMMARY

In one embodiment, a method includes acquiring a plurality of ultrasoundimages of an anatomical region, each ultrasound image acquired at adifferent combination of parameter values for a first scan parameter anda second scan parameter, selecting a first parameter value for the firstscan parameter and a second parameter value for the second scanparameter based on an image quality of each image, and acquiring one ormore additional ultrasound images at the first parameter value and thesecond parameter value.

The above advantages and other advantages, and features of the presentdescription will be readily apparent from the following DetailedDescription when taken alone or in connection with the accompanyingdrawings. It should be understood that the summary above is provided tointroduce in simplified form a selection of concepts that are furtherdescribed in the detailed description. It is not meant to identify keyor essential features of the claimed subject matter, the scope of whichis defined uniquely by the claims that follow the detailed description.Furthermore, the claimed subject matter is not limited toimplementations that solve any disadvantages noted above or in any partof this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects of this disclosure may be better understood upon readingthe following detailed description and upon reference to the drawings inwhich:

FIG. 1 shows a block diagram of an exemplary embodiment of an ultrasoundsystem;

FIG. 2 is a schematic diagram illustrating a system for generatingultrasound images at optimized parameter settings, according to anexemplary embodiment;

FIG. 3 is a schematic diagram illustrating a process for selectingultrasound scan parameters, according to an exemplary embodiment;

FIG. 4 shows example ultrasound images and associated image qualitymetrics that may be acquired according to the process of FIG. 3; and

FIGS. 5A and 5B are a flow chart illustrating an example method forselecting ultrasound scan parameters during ultrasound imaging,according to an exemplary embodiment.

FIG. 6 shows an example table of ultrasound scan parameters to beapplied to acquire ultrasound images usable to select the ultrasoundscan parameters, according to the method of FIGS. 5A and 5B.

FIG. 7 shows two example timings of image acquisition relative to acardiac cycle.

FIG. 8 is a flow chart illustrating an example method for selectingimage post-acquisition processing parameters during ultrasound image.

FIG. 9 is a graph showing a scan sequence for acquiring a singleultrasound image, according to an exemplary embodiment.

FIG. 10 is a graph showing a scan sequence for acquiring multipleultrasound images, according to an exemplary embodiment.

DETAILED DESCRIPTION

Medical ultrasound imaging typically includes the placement of anultrasound probe including one or more transducer elements onto animaging subject, such as a patient, at the location of a targetanatomical feature (e.g., abdomen, chest, etc.). Images are acquired bythe ultrasound probe and are displayed on a display device in real timeor near real time (e.g., the images are displayed once the images aregenerated and without intentional delay). The operator of the ultrasoundprobe may view the images and adjust various acquisition parametersand/or the position of the ultrasound probe in order to obtainhigh-quality images of the target anatomical feature (e.g., the heart,the liver, the kidney, or another anatomical feature). The acquisitionparameters that may be adjusted include transmit frequency, transmitdepth, gain, beam steering angle, beamforming strategy, and/or otherparameters. Varying the acquisition parameters to acquire an optimalimage (e.g., of desired quality) can be very challenging and is based onuser experience. Image quality variations with acquisition parameters isnot a well-studied problem. Thus, the adjustment of the acquisitionparameters by the operator in order to acquire an optimal image is oftensubjective. For example, the operator may adjust various acquisitionparameters until an image is acquired that looks optimal to theoperator, and the process of adjusting the acquisition parameters maynot be defined or repeated from exam to exam. Further, variouspost-acquisition image parameters that may affect image quality are alsoadjustable by the operator, such as bandwidth and center frequency ofthe filtering of the received ultrasound data. This subjectivity andlack of a defined process may lead to irreproducible results and, inmany ultrasound exams, images that are as high quality as possible maynot be acquired.

Thus, according to embodiments disclosed herein, the problem of imageacquisition parameter optimization and/or image post-acquisitionprocessing optimization is addressed via a feedback system based on anautomated image quality measurement algorithm that is configured toautomatically identify the acquisition parameters that will generate thebest possible image for the anatomy being imaged. The automated imagequality measurement algorithm may include an artificialintelligence-assisted feedback system to optimize the acquisitionparameters in a joint fashion, with a plurality of images each acquiredat a different combination of scan parameter values to arrive at anoptimal acquisition parameter setting based on automatically identifiedimage quality metrics. For example, a set of images and/or cine loopsmay be acquired, each at a different possible combination of transmitdepth and transmit frequency. The image or cine loop that has thehighest image quality (e.g., as detected by the artificial intelligencebased system) may be identified and the optimal transmit depth andoptimal transmit frequency may be set as the depth and frequency atwhich the identified, highest-quality image was acquired. In doing so,the optimal acquisition parameters (e.g., depth and frequency) for agiven scan plane/anatomical feature may be identified in a reproduciblemanner, which may increase consistency of image quality across differentultrasound exams. Additionally, in some examples, different parametervalues for one or more post-acquisition processing parameters may beapplied to an image to generate, for each post-acquisition processingparameter, a set of replicate images that each have a differentparameter value for that post-acquisition processing parameter. Theimage quality may be determined for each replicate image, and thereplicate image having the highest image quality may be selected. Theparameter value for that post-acquisition processing parameter may beset as the parameter value from the selected replicate image and appliedto subsequent images. The selection of the optimal acquisition and/orpost-acquisition parameters may simplify the operator's workflow, whichmay reduce exam time and may facilitate higher quality exams, even formore novice operators.

An example ultrasound system including an ultrasound probe, a displaydevice, and an imaging processing system are shown in FIG. 1. Via theultrasound probe, ultrasound images may be acquired and displayed on thedisplay device. The images may be acquired using various scanparameters, such as frequency and depth, which have parameter valuesthat may be selected to increase image quality of the acquired images.To select the scan parameter values, a plurality of images acquired atdifferent scan parameter values may be analyzed to determine which scanparameter values result in images having a highest image quality. Animage processing system, as shown in FIG. 2, includes one or more imagequality models, such as a depth model and one or more frequency models,which may be deployed according to the joint process shown in FIG. 3 todetermine the image quality of each of the plurality of images, as shownin FIG. 4, and select scan parameter values that will result inrelatively high image quality. A method for jointly selecting scanparameter values during ultrasound imaging is shown in FIGS. 5A and 5B.The method for jointly selecting scan parameter values may includeapplication of a table of scan parameter values, such as the table shownin FIG. 6, which may result in different scan parameters being adjustedin different orders, such as the ordering of frequency values shown inFIG. 7. After the target scan parameter values have been selected,different post-acquisition processing parameter values may be applied toan image to create a set of adjusted images, and the image qualitymodels may be deployed to determine which image from the set of adjustedimages has the highest image quality and thus which post-acquisitionprocessing parameter value(s) should be applied to that and/orsubsequent images, as shown by the method of FIG. 8. The imagesdisclosed herein may be acquired according to a scan sequence as shownin FIG. 9 and/or according to a scan sequence as shown in FIG. 10.

Referring to FIG. 1, a schematic diagram of an ultrasound imaging system100 in accordance with an embodiment of the disclosure is shown. Theultrasound imaging system 100 includes a transmit beamformer 101 and atransmitter 102 that drives elements (e.g., transducer elements) 104within a transducer array, herein referred to as probe 106, to emitpulsed ultrasonic signals (referred to herein as transmit pulses) into abody (not shown). According to an embodiment, the probe 106 may be aone-dimensional transducer array probe. However, in some embodiments,the probe 106 may be a two-dimensional matrix transducer array probe. Asexplained further below, the transducer elements 104 may be comprised ofa piezoelectric material. When a voltage is applied to a piezoelectriccrystal, the crystal physically expands and contracts, emitting anultrasonic spherical wave. In this way, transducer elements 104 mayconvert electronic transmit signals into acoustic transmit beams.

After the elements 104 of the probe 106 emit pulsed ultrasonic signalsinto a body (of a patient), the pulsed ultrasonic signals areback-scattered from structures within an interior of the body, likeblood cells or muscular tissue, to produce echoes that return to theelements 104. The echoes are converted into electrical signals, orultrasound data, by the elements 104 and the electrical signals arereceived by a receiver 108. The electrical signals representing thereceived echoes are passed through a receive beamformer 110 that outputsultrasound data. Additionally, transducer element 104 may produce one ormore ultrasonic pulses to form one or more transmit beams in accordancewith the received echoes.

According to some embodiments, the probe 106 may contain electroniccircuitry to do all or part of the transmit beamforming and/or thereceive beamforming. For example, all or part of the transmit beamformer101, the transmitter 102, the receiver 108, and the receive beamformer110 may be situated within the probe 106. The terms “scan” or “scanning”may also be used in this disclosure to refer to acquiring data throughthe process of transmitting and receiving ultrasonic signals. The term“data” may be used in this disclosure to refer to either one or moredatasets acquired with an ultrasound imaging system. In one embodiment,data acquired via ultrasound system 100 may be used to train a machinelearning model. A user interface 115 may be used to control operation ofthe ultrasound imaging system 100, including to control the input ofpatient data (e.g., patient medical history), to change a scanning ordisplay parameter, to initiate a probe repolarization sequence, and thelike. The user interface 115 may include one or more of the following: arotary element, a mouse, a keyboard, a trackball, hard keys linked tospecific actions, soft keys that may be configured to control differentfunctions, and a graphical user interface displayed on a display device118.

The ultrasound imaging system 100 also includes a processor 116 tocontrol the transmit beamformer 101, the transmitter 102, the receiver108, and the receive beamformer 110. The processer 116 is in electroniccommunication (e.g., communicatively connected) with the probe 106. Forpurposes of this disclosure, the term “electronic communication” may bedefined to include both wired and wireless communications. The processor116 may control the probe 106 to acquire data according to instructionsstored on a memory of the processor, and/or memory 120. The processor116 controls which of the elements 104 are active and the shape of abeam emitted from the probe 106. The processor 116 is also in electroniccommunication with the display device 118, and the processor 116 mayprocess the data (e.g., ultrasound data) into images for display on thedisplay device 118. The processor 116 may include a central processor(CPU), according to an embodiment. According to other embodiments, theprocessor 116 may include other electronic components capable ofcarrying out processing functions, such as a digital signal processor, afield-programmable gate array (FPGA), or a graphic board. According toother embodiments, the processor 116 may include multiple electroniccomponents capable of carrying out processing functions. For example,the processor 116 may include two or more electronic components selectedfrom a list of electronic components including: a central processor, adigital signal processor, a field-programmable gate array, and a graphicboard. According to another embodiment, the processor 116 may alsoinclude a complex demodulator (not shown) that demodulates the RF dataand generates raw data. In another embodiment, the demodulation can becarried out earlier in the processing chain. The processor 116 isadapted to perform one or more processing operations according to aplurality of selectable ultrasound modalities on the data. In oneexample, the data may be processed in real-time during a scanningsession as the echo signals are received by receiver 108 and transmittedto processor 116. For the purposes of this disclosure, the term“real-time” is defined to include a procedure that is performed withoutany intentional delay. For example, an embodiment may acquire images ata real-time rate of 7-20 frames/sec. The ultrasound imaging system 100may acquire 2D data of one or more planes at a significantly fasterrate. However, it should be understood that the real-time frame-rate maybe dependent on the length of time that it takes to acquire each frameof data for display. Accordingly, when acquiring a relatively largeamount of data, the real-time frame-rate may be slower. Thus, someembodiments may have real-time frame-rates that are considerably fasterthan 20 frames/sec while other embodiments may have real-timeframe-rates slower than 7 frames/sec. The data may be stored temporarilyin a buffer (not shown) during a scanning session and processed in lessthan real-time in a live or off-line operation. Some embodiments of theinvention may include multiple processors (not shown) to handle theprocessing tasks that are handled by processor 116 according to theexemplary embodiment described hereinabove. For example, a firstprocessor may be utilized to demodulate and decimate the RF signal whilea second processor may be used to further process the data, for exampleby augmenting the data as described further herein, prior to displayingan image. It should be appreciated that other embodiments may use adifferent arrangement of processors.

The ultrasound imaging system 100 may continuously acquire data at aframe-rate of, for example, 10 Hz to 30 Hz (e.g., 10 to 30 frames persecond). Images generated from the data may be refreshed at a similarframe-rate on display device 118. Other embodiments may acquire anddisplay data at different rates. For example, some embodiments mayacquire data at a frame-rate of less than 10 Hz or greater than 30 Hzdepending on the size of the frame and the intended application. Amemory 120 is included for storing processed frames of acquired data. Inan exemplary embodiment, the memory 120 is of sufficient capacity tostore at least several seconds' worth of frames of ultrasound data. Theframes of data are stored in a manner to facilitate retrieval thereofaccording to its order or time of acquisition. The memory 120 maycomprise any known data storage medium.

In various embodiments of the present invention, data may be processedin different mode-related modules by the processor 116 (e.g., B-mode,Color Doppler, M-mode, Color M-mode, spectral Doppler, Elastography,TVI, strain, strain rate, and the like) to form 2D or 3D data. Forexample, one or more modules may generate B-mode, color Doppler, M-mode,color M-mode, spectral Doppler, Elastography, TVI, strain, strain rate,and combinations thereof, and the like. As one example, the one or moremodules may process color Doppler data, which may include traditionalcolor flow Doppler, power Doppler, HD flow, and the like. The imagelines and/or frames are stored in memory and may include timinginformation indicating a time at which the image lines and/or frameswere stored in memory. The modules may include, for example, a scanconversion module to perform scan conversion operations to convert theacquired images from beam space coordinates to display spacecoordinates. A video processor module may be provided that reads theacquired images from a memory and displays an image in real time while aprocedure (e.g., ultrasound imaging) is being performed on a patient.The video processor module may include a separate image memory, and theultrasound images may be written to the image memory in order to be readand displayed by display device 118.

In various embodiments of the present disclosure, one or more componentsof ultrasound imaging system 100 may be included in a portable, handheldultrasound imaging device. For example, display device 118 and userinterface 115 may be integrated into an exterior surface of the handheldultrasound imaging device, which may further contain processor 116 andmemory 120. Probe 106 may comprise a handheld probe in electroniccommunication with the handheld ultrasound imaging device to collect rawultrasound data. Transmit beamformer 101, transmitter 102, receiver 108,and receive beamformer 110 may be included in the same or differentportions of the ultrasound imaging system 100. For example, transmitbeamformer 101, transmitter 102, receiver 108, and receive beamformer110 may be included in the handheld ultrasound imaging device, theprobe, and combinations thereof.

After performing a two-dimensional ultrasound scan, a block of datacomprising scan lines and their samples is generated. After back-endfilters are applied, a process known as scan conversion is performed totransform the two-dimensional data block into a displayable bitmap imagewith additional scan information such as depths, angles of each scanline, and so on. During scan conversion, an interpolation technique isapplied to fill missing holes (i.e., pixels) in the resulting image.These missing pixels occur because each element of the two-dimensionalblock should typically cover many pixels in the resulting image. Forexample, in current ultrasound imaging systems, a bicubic interpolationis applied which leverages neighboring elements of the two-dimensionalblock. As a result, if the two-dimensional block is relatively small incomparison to the size of the bitmap image, the scan-converted imagewill include areas of poor or low resolution, especially for areas ofgreater depth.

Ultrasound images acquired by ultrasound imaging system 100 may befurther processed. In some embodiments, ultrasound images produced byultrasound imaging system 100 may be transmitted to an image processingsystem, where in some embodiments, the ultrasound images may be analyzedby one or more machine learning models trained using ultrasound imagesand corresponding ground truth output in order to assign scanparameter-specific image quality metrics to the ultrasound images. Asused herein, ground truth output refers to an expected or “correct”output based on a given input into a machine learning model. Forexample, if a machine learning model is being trained to classify imagesof cats, the ground truth output for the model, when fed an image of acat, is the label “cat”. As explained in more detail below, if a machinelearning model is being trained to classify ultrasound images on thebasis of an image quality factor associated with depth (e.g., visibilityof certain anatomical features), the ground truth output for the modelmay be a label indicating a level of the image quality factor, e.g., ona scale of 1-5 with 1 being a lowest image quality level (e.g.,reflecting insufficient or inadequate depth, the least optimal depth)and 5 being a highest image quality level (e.g., reflecting sufficientdepth, the most optimal depth). Similarly, if a machine learning modelis being trained to classify ultrasound images on the basis of an imagequality factor associated with frequency (e.g., speckling), the groundtruth output for the model may be a label indicating a level of theimage quality factor, e.g., on a scale of 1-5 with 1 being a lowestimage quality level (e.g., reflecting high/not smooth speckling, theleast optimal frequency) and 5 being a highest image quality level(e.g., reflecting low/smooth speckling, the most optimal frequency).

Although described herein as separate systems, it will be appreciatedthat in some embodiments, ultrasound imaging system 100 includes animage processing system. In other embodiments, ultrasound imaging system100 and the image processing system may comprise separate devices. Insome embodiments, images produced by ultrasound imaging system 100 maybe used as a training data set for training one or more machine learningmodels, wherein the machine learning models may be used to perform oneor more steps of ultrasound image processing, as described below.

Referring to FIG. 2, image processing system 202 is shown, in accordancewith an exemplary embodiment. In some embodiments, image processingsystem 202 is incorporated into the ultrasound imaging system 100. Forexample, the image processing system 202 may be provided in theultrasound imaging system 100 as the processor 116 and memory 120. Insome embodiments, at least a portion of image processing 202 is disposedat a device (e.g., edge device, server, etc.) communicably coupled tothe ultrasound imaging system via wired and/or wireless connections. Insome embodiments, at least a portion of image processing system 202 isdisposed at a separate device (e.g., a workstation) which can receiveimages/maps from the ultrasound imaging system or from a storage devicewhich stores the images/data generated by the ultrasound imaging system.Image processing system 202 may be operably/communicatively coupled to auser input device 232 and a display device 234. The user input device232 may comprise the user interface 115 of the ultrasound imaging system100, while the display device 234 may comprise the display device 118 ofthe ultrasound imaging system 100, at least in some examples.

Image processing system 202 includes a processor 204 configured toexecute machine readable instructions stored in non-transitory memory206. Processor 204 may be single core or multi-core, and the programsexecuted thereon may be configured for parallel or distributedprocessing. In some embodiments, the processor 204 may optionallyinclude individual components that are distributed throughout two ormore devices, which may be remotely located and/or configured forcoordinated processing. In some embodiments, one or more aspects of theprocessor 204 may be virtualized and executed by remotely-accessiblenetworked computing devices configured in a cloud computingconfiguration.

Non-transitory memory 206 may store image quality models 208, trainingmodule 210, and ultrasound image data 212. Image quality models 208 mayinclude one or more machine learning models, such as deep learningnetworks, comprising a plurality of weights and biases, activationfunctions, loss functions, gradient descent algorithms, and instructionsfor implementing the one or more deep neural networks to process inputultrasound images. For example, image quality models 208 may storeinstructions for implementing a depth model 209 and/or one or morefrequency models 211. The depth model 209 and one or more frequencymodels 211 may each include one or more neural networks. Image qualitymodels 208 may include trained and/or untrained neural networks and mayfurther include training routines, or parameters (e.g., weights andbiases), associated with one or more neural network models storedtherein.

Depth model 209 may be a neural network (e.g., a convolutional neuralnetwork) trained to identify far-field structures in the ultrasoundimages and determine if far-field structures (e.g., structuresbeyond/below the focal point of the ultrasound beam with respect to thetransducers of the ultrasound probe) are at an expected depth. Depthmodel 209 may be trained to identify the far-field structures in a scanplane/view specific manner. For example, a depth model may be trained toidentify far-field structures in a four-chamber view of a heart but notin a parasternal long axis (PLAX) view of the heart. Thus, in someexamples, depth model 209 may actually comprise a plurality of depthmodels, each specific to a different scan plane or anatomical view.Depth model 209 may be trained to output a first image quality metricthat reflects a quality of an input ultrasound image as a function oftransmit acquisition depth. For example, the far-field structuresidentified by the depth model may change in appearance/visibility asdepth is changed, and the first image quality metric output by the depthmodel may reflect the appearance/visibility of these structures as anindicator of whether the depth used to acquire the ultrasound image isan optimal depth.

The one or more frequency models 211 may include one or more neuralnetworks or other machine learning models trained to output a respectivesecond image quality metric that represents an image quality factor thatchanges as a function of transmit frequency. The one or more frequencymodels 211 may include a first frequency model that assesses specklesize (referred to as a speckle model), a second frequency model thatassess key landmarks (referred to as a landmark detection model), and athird frequency model that assess global image quality relative to apopulation-wide library of ultrasound images (referred to as a globalimage quality model). The speckle model may be trained to output aspeckle image quality metric that reflects a level of smoothness ofspeckling in the input ultrasound image. As speckling smoothnessincreases as frequency increases, the speckle image quality metric mayincrease as frequency increases. The landmark detection model may betrained to output a landmark image quality metric that reflects theappearance/visibility of certain anatomical features (landmarks) in theinput ultrasound image. For example, as transmit frequency increases,certain anatomical features, such as the valves in the four-chamberview, may start to decrease in image quality/appearance. Thus, thelandmark detection model may identify the key landmarks in the inputultrasound image and output the landmark image quality metric based onthe image quality/visibility of the identified key landmarks. Becausethe key landmarks change as the scan plane/anatomical view change, thelandmark detection model may include a plurality of different landmarkdetection models, each specific to a different scan plane or anatomicalview.

The global image quality model may be trained to assess the overallimage quality of an input ultrasound image relative to a population-widelibrary of ultrasound images. For example, the global image qualitymodel may be trained with a plurality of ultrasound images of aplurality of different patients, with each training ultrasound imageannotated or labeled by an expert (e.g., cardiologist or otherclinician) with an overall image quality score (e.g., on a scale of 1-5with 1 being a lowest image quality and 5 being a highest imagequality). The global image quality model, after training/validation, maythen generate an output of a global image quality metric that reflectsthe overall image quality of an input ultrasound image relative to thetraining ultrasound images. By including an overall image quality metricthat is not specifically affected by depth or frequency,patient-specific image quality issues may be accounted for.

Non-transitory memory 206 may further include training module 210, whichcomprises instructions for training one or more of the machine learningmodels stored in image quality models 208. In some embodiments, thetraining module 210 is not disposed at the image processing system 202.The image quality models 208 thus includes trained and validatednetwork(s).

Non-transitory memory 206 may further store ultrasound image data 212,such as ultrasound images captured by the ultrasound imaging system 100of FIG. 1. The ultrasound image data 212 may comprise ultrasound imagedata as acquired by the ultrasound imaging system 100, for example. Theultrasound images of the ultrasound image data 212 may compriseultrasound images that have been acquired by the ultrasound imagingsystem 100 at different parameter values for different scan parameters,such as different frequencies and/or different depths. Further,ultrasound image data 212 may store ultrasound images, ground truthoutput, iterations of machine learning model output, and other types ofultrasound image data that may be used to train the image quality models208, when training module 210 is stored in non-transitory memory 206. Insome embodiments, ultrasound image data 212 may store ultrasound imagesand ground truth output in an ordered format, such that each ultrasoundimage is associated with one or more corresponding ground truth outputs.However, in examples where training module 210 is not disposed at theimage processing system 202, the images/ground truth output usable fortraining the image quality models 210 may be stored elsewhere.

In some embodiments, the non-transitory memory 206 may includecomponents disposed at two or more devices, which may be remotelylocated and/or configured for coordinated processing. In someembodiments, one or more aspects of the non-transitory memory 206 mayinclude remotely-accessible networked storage devices configured in acloud computing configuration.

User input device 232 may comprise one or more of a touchscreen, akeyboard, a mouse, a trackpad, a motion sensing camera, or other deviceconfigured to enable a user to interact with and manipulate data withinimage processing system 202. In one example, user input device 232 mayenable a user to make a selection of an ultrasound image to use intraining a machine learning model, to indicate or label a position of aninterventional device in the ultrasound image data 212, or for furtherprocessing using a trained machine learning model.

Display device 234 may include one or more display devices utilizingvirtually any type of technology. In some embodiments, display device234 may comprise a computer monitor, and may display ultrasound images.Display device 234 may be combined with processor 204, non-transitorymemory 206, and/or user input device 232 in a shared enclosure, or maybe peripheral display devices and may comprise a monitor, touchscreen,projector, or other display device known in the art, which may enable auser to view ultrasound images produced by an ultrasound imaging system,and/or interact with various data stored in non-transitory memory 206.

It should be understood that image processing system 202 shown in FIG. 2is for illustration, not for limitation. Another appropriate imageprocessing system may include more, fewer, or different components.

Turning to FIG. 3, it shows a process 300 for jointly selecting scanparameter values for two scan parameters, herein depth and frequency.The scan parameter values may be selected according to which scanparameter values result in a highest image quality, where the imagequality is determined by machine learning models. Process 300 may becarried out with the components of FIGS. 1-2, e.g., images may beacquired via ultrasound probe 106 and the parameter value selection maybe carried out by image processing system 202. As shown, imageacquisition may commence at 304 once the operator of the ultrasoundsystem positions the ultrasound probe to image a target scan plane(shown at 302). The image acquisition may include acquiring a pluralityof images each at a different combination of depth and frequency. Forexample, a first plurality of images may be acquired with each image ofthe first plurality of images acquired at a single, first frequency(e.g., 1.4 MHz) from a set of frequencies, and a different depth valuefrom a set of depth values (e.g., a first image at 30 cm, a second imageat 17 cm, and a third image at 10 cm); a second plurality of images maybe acquired with each image of the second plurality of images at asingle, second frequency (e.g., 1.7 MHz) from the set of frequencies,and a different depth value from the set of depth values; and one ormore additional pluralities of images may be acquired, with eachadditional plurality of images acquired at next frequency (e.g., a oneat 2 MHz and one at 2.3 MHz) in the set of frequencies and at thedifferent depth values. In this way, an image may be acquired for eachpossible combination of depths and frequencies from the set of depthsand the set of frequencies.

Each image of the plurality of images is entered into a plurality ofmodels, including a depth model 306, which may be a non-limiting exampleof depth model 209 of FIG. 2. The depth model 306 may be a machinelearning model, such as a neural network, trained to determine a firstimage quality score that reflects a quality of an input image from theperspective of whether one or more key anatomical features in the targetscan plane are sufficiently visible/of high enough quality. The one ormore key anatomical features may be features that change invisibility/quality as depth changes, and thus may be used as a markerfor which depth value may result in a high quality image.

The one or more models may include a global image quality model 310, alandmark detection model 308, and a speckle model 312 (which may benon-limiting examples of the global image quality model, landmarkdetection model, and speckle model described above with respect to FIG.2). Each of the global image quality model 310, landmark detection model308, and speckle size model 312 may be a machine learning model, such asa neural network. The global image quality model 310 may be trained toassign a score (e.g., on a scale of 1-5) to each image based on theimage quality of that image in a population-wide manner. The landmarkdetection model 308 and speckle size model 312 may each output scoresthat reflect how much a given patient's images change in quality as afunction of ultrasound transmit frequency, as speckles on ultrasoundimages may smooth/change in size as frequency increases and certain keyanatomical landmarks may become more or less visible as frequencychanges. The scores output from each of the global image quality model,landmark detection model, and speckle size model may be combined togenerate a cumulative, second image quality score. The image qualitymetrics (e.g., from each of the models discussed herein) may be outputat 314.

Thus, after each image is input into the models, each image may beassigned two scores, a first image quality score output from the depthmodel 306 and a second image quality score that is a cumulative scorecalculated from the output of each of the global image quality model310, the landmark detection model 308, and the speckle size model 312.The image from the plurality of images having the highest combined imagequality score (e.g., the first image quality score combined with thesecond image quality score) may be identified as the selected image, asshown at 316. If there are two or more images having the same, highestcombined image quality score, the image acquired at the highestfrequency may be selected. The depth used to acquire the selected imagemay be set as the selected depth value and the frequency used to acquirethe selected image may be set as the selected frequency value. Anyadditional images of that target scan plane or view desired by theoperator and/or dictated by a scanning protocol may be acquired at theselected depth value and the selected frequency value, as shown at 318.According to the joint process shown in FIG. 3, the depth value andfrequency value are only selected once all images have been acquired.

The joint process shown in FIG. 3 may result in a total of m×n imagesbeing acquired in order to select the depth value and frequency valuefor subsequent acquisition. The m number of images may be based on howmany depth values are available/selected to be optimized. As describedabove, there may be three possible depth values, but other numbers ofdepth values may be possible, such as two or four or more. Likewise, then number of images may be based on how many frequency values areavailable/selected to be optimized. As described above, there may befour possible frequency values, but other numbers of frequency valuesmay be possible, such as three, five, etc. Further, while FIG. 3 shows aprocess for selecting values of two scan parameters, more scanparameters may be selected/optimized, such as gain, beam steering,beamforming strategy, filtering, etc. In such examples, the number ofacquired images may be m×n×1, where the 1 number of images is based onthe values of gain (or other parameter, such beam steering) available tobe optimized. In this way, an image may be acquired for each possiblecombination of depth, frequency, and/or any other acquisition parametervalues.

The joint process described above may result in more images beingacquired than a sequential process where m+n images are acquired and afirst scan parameter is selected (e.g., depth) before a second scanparameter is selected (e.g., frequency), which may make the sequentialprocess more practical and easier to implement than a joint process.However, the overall time to select the parameter values for the scanparameters may be lower for the joint process. Further, when scanparameters are dependent on each other, the joint process, where allpossible combinations are acquired, may reveal any unexpectedcombinations that result in high image quality, while the sequentialprocess may leave out possible combinations on the assumption thatchanging frequency (for example) will not affect depth-based imagequality.

FIG. 4 shows a plurality of ultrasound images 400 that may be acquiredand analyzed during the process illustrated in FIG. 3. The plurality ofultrasound images 400 includes a first plurality of images 401. Eachimage of the first plurality of images 401 was acquired at a differentdepth value (e.g., a first image 402 acquired at 30 cm, a second image404 acquired at 17 cm, and a third image 406 acquired at 10 cm) and atthe same first frequency (e.g., 1.4 MHz). The plurality of ultrasoundimages 400 further includes a second plurality of images 410. Each imageof the second plurality of images 410 was acquired at a different depthvalue and at the same second frequency (e.g., (e.g., a first image 412acquired at 30 cm, a second image 404 acquired at 17 cm, and a thirdimage 406 acquired at 10 cm, which of these images acquired at 1.7 MHz).The plurality of ultrasound images 400 further includes a thirdplurality of images 420. Each image of the third plurality of images 420was acquired at a different depth value and at the same third frequency(e.g., (e.g., a first image 422 acquired at 30 cm, a second image 424acquired at 17 cm, and a third image 426 acquired at 10 cm, which eachof these images acquired at 2.0 MHz). The plurality of ultrasound images400 further includes a fourth plurality of images 430. Each image of thefourth plurality of images 430 was acquired at a different depth valueand at the same fourth frequency (e.g., (e.g., a first image 432acquired at 30 cm, a second image 434 acquired at 17 cm, and a thirdimage 436 acquired at 10 cm, with each of these images acquired at 2.3MHz).

Each image shown in FIG. 4 includes two quality indicators, a predictedIQ rating and a cumulative score. The cumulative score may be determinedbased on output from one or more of the models illustrated in FIG. 3,such as the depth model, the landmark model, the global image qualitymodel, and the speckle size model. The predicted IQ rating may beobtained from the cumulative scores using empirical thresholdsdetermined during the training process. The predicted IQ ratingclassifies the input ultrasound image or cine loop into three levels ofimage quality, such as 1 for bad/low image quality, 2 for acceptableimage quality, and 3 for good/high image quality, while the cumulativescore is a continuous number which is used to select the bestacquisition from a set of acquisitions.

The image having the highest combined score (e.g., the predicted IQscore and the cumulative score) from the plurality of images may beselected, and the selected depth value and selected frequency value maybe the depth value and frequency value used to acquire the selectedimage. For example, as shown, image 404 has a predicted IQ score of 3and a cumulative score of 6, which is higher than the scores of theremaining images. Thus, the selected depth value may be 17 cm, as image404 was acquired with a depth value of 17 cm, and the selected frequencymay be 1.4 MHz, as image 404 was acquired with a frequency value of 1.4MHz.

FIGS. 5A and 5B show flow chart illustrating an example method 500 forjoint ultrasound imaging parameter selection according to an embodiment.In particular, method 500 relates to acquiring a plurality of ultrasoundimages at different parameter values of difference scan parameters andthen processing the acquired ultrasound images with multiple machinelearning models to select optimal scan parameter values, which mayimprove the image quality of displayed ultrasound images. Method 500 isdescribed with regard to the systems and components of FIGS. 1-2, thoughit should be appreciated that the method 500 may be implemented withother systems and components without departing from the scope of thepresent disclosure. Method 500 may be carried out according toinstructions stored in non-transitory memory of a computing device, suchas image processing system 202 of FIG. 2.

At 502, ultrasound images are acquired and displayed on a displaydevice. For example, the ultrasound images may be acquired with theultrasound probe 106 of FIG. 1 and displayed to an operator via displaydevice 118. The images may be acquired and displayed in real time ornear real time, and may be acquired with default or user-specified scanparameters (e.g., default depth, frequency, etc.). At 504, method 500determines if an indication that a target scan plane is being imaged hasbeen received. The target scan plane may be a scan plane specified by ascanning protocol or designated by an operator as being a target scanplane. For example, the ultrasound images may be acquired as part of anultrasound exam where certain anatomical features are imaged in certainviews/axes in order to diagnose a patient condition, measure aspects ofthe anatomical features, etc. For example, during a cardiac exam, one ormore target scan planes (also referred to as views) of the heart of apatient may be imaged. The target scan planes may include a four-chamberview, a two-chamber view (which may also be referred to as a short axisview), and a long axis view (which may also be referred to as a PLAXview or three-chamber view). One or more images may be acquired in eachscan plane and saved as part of the exam for later analysis by aclinician such as a cardiologist. When acquiring the images for theexam, the ultrasound operator (e.g., sonographer) may move theultrasound probe until the operator determines that the target scanplane is being imaged, and then the operator may enter an input (e.g.,via user interface 115) indicating that the target scan plane is beingimaged. In another example, the ultrasound system (e.g., via the imageprocessing system 202) may automatically determine that the target scanplane is being imaged. For example, each acquired ultrasound image (orsome frequency of the acquired ultrasound images, such as every fifthimage) may be entered into a detection model that is configured toautomatically detect the current scan plane. If the current scan planeis the target scan plane, the indication may be generated.

If an indication that the target scan plane is being imaged is notreceived, method 500 returns to 502 to continue to acquire and displayultrasound images (e.g., at the default or user-set scan parameters). Ifan indication is received that the target scan plane is being imaged,method 500 proceeds to 505 to optionally select a parameter selectionacquisition protocol according to the target plane. When the parameterselection acquisition protocol is carried out, a plurality of images areacquired (e.g., in a sequential manner), where two or more scanparameters (such as depth and frequency) are varied for each image, suchthat images are acquired at all possible combinations of parametervalues. The relative order of the image acquisition may be varied basedon the target scan plane in some examples. For example, the images maybe acquired according to a first acquisition protocol where depth isheld constant at a first value while a first set of images is acquiredeach at a different frequency value, then depth is changed to a secondvalue and held constant while a second set of images is acquired each ata different frequency value, then depth is changed to a third value andheld constant while a third set of images is acquired each at adifferent frequency value, and so forth. In the second acquisitionprotocol, frequency is held constant at a first value while a first setof images is acquired each at a different depth value, then frequency ischanged to a second value and held constant while a second set of imagesis acquired each at a different depth value, then frequency is changedto a third value and held constant while a third set of images isacquired each at a different depth value, etc. Other acquisitionprotocols are possible, such as more random distribution in the changesin both depth and frequency rather the more ordered combinationsdescribed above with respect to the first acquisition and secondacquisition protocol.

The decision of which acquisition protocol to carry out may be based onthe target scan plane. For example, the first acquisition protocol maybe carried out when imaging a four-chamber view of the heart while thesecond acquisition protocol may be carried out when imaging a PLAX viewof the heart. As will be explained in more detail below with respect toFIG. 7, the second acquisition protocol may result in all images havinga first frequency value being acquired during the same relative phase ofthe cardiac cycle, all images having a second frequency value beingacquired during a different phase of the cardiac cycle than the imagesacquired at the first frequency, but the images acquired at the secondfrequency are all acquired at the same relative phase to each other, andso forth. When comparing images acquired at different frequency valuesbut the same depth value, the fact that the images are also taken atdifferent phases of the cardiac cycle may result in some of thefrequency-based changes being confounded or harder to detect. However,the opposite may be true of depth-related changes. In contrast, thefirst acquisition protocol may result in a first set of images, acquiredat the same depth and different frequencies, to be acquired at the samerelative cardiac phase, a second set of images (acquired at the same,next depth and different frequencies) to be acquired at the samerelative cardiac phase (though different than the first set), etc. Whencomparing images acquired at different frequency values but the samedepth value, the fact that the images are taken at the same relativephase of the cardiac cycle may result in fewer of the frequency-basedchanges being confounded or harder to detect. However, the opposite maybe true of depth-related changes. Thus, the decision of whichacquisition protocol to select may be based on whether depth-based orfrequency-based changes are more important to detect when selecting theparameter values, as well as the type of anatomy being scanned (e.g.,whether the anatomy exhibits periodic motion). Once an acquisitionprotocol is selected, the images may be acquired according to theprotocol, as explained below. However, in some examples only oneprotocol may be available, and thus there may be no selection process.

At 506, a first set of images is acquired, with each image acquired at afirst parameter value of a first set of parameter values for a firstscan parameter and a different parameter value of a second set ofparameter values for a second scan parameter. For example, the firstscan parameter may be depth and the first set of parameter values may bethe different depth values described above (e.g., 10 cm, 17 cm, and 30cm). Thus, the first set of images may be acquired at one of the depthvalues (e.g., 10 cm). The second scan parameter may be frequency and thesecond set of parameter values may be the different frequenciesdescribed above (e.g., 1.4 MHz, 1.7 MHz, 2 MHz, and 2.3 MHz). Thus, eachimage of the first set of images may be acquired at a different one ofthe second set of parameter values (e.g., one at 1.4 MHz, one at 1.7MHz, one at 2 MHz, and one at 2.3 MHz). The first set of images mayinclude four images, or more than four images if more than fourfrequency values are to be selected from. During acquisition of thefirst set of images, any other scan parameters (e.g., gain, etc.) thatmay be optimized may be held constant. Further, during acquisition ofthe first set of images, the acquired images may be displayed on thedisplay device at the frame rate at which the images are acquired, atleast in some examples. In some examples, a set of cine loops may beacquired, or a mix of images and cine loops. When cine loops areacquired, the acquisition of the different cine loops may be carried outas explained above, e.g., at the same depth value and at differentfrequency values.

At 508, a second set of images (and/or cine loops) are acquired, each ata second parameter value of the first set of parameter values for thefirst scan parameter and a different parameter value of the second setof parameter values for the second scan parameter. For example, eachimage the second set of images may be acquired at one of the depthvalues (e.g., 17 cm) that is different than the first value describedabove. Each image of the second set of images may be acquired at adifferent one of the second set of parameter values (e.g., one at 1.4MHz, one at 1.7 MHz, one at 2 MHz, and one at 2.3 MHz). The second setof images may include four images, or more than four images if more thanfour frequency values are to be selected from. During acquisition of thesecond set of images, any other scan parameters (e.g., gain, etc.) thatmay be optimized may be held constant. Further, during acquisition ofthe second set of images, the acquired images may be displayed on thedisplay device at the frame rate at which the images are acquired, atleast in some examples.

At 510, method 500 includes determining if a set of images has beenacquired for each parameter value of the first set of parameter values.For example, the method may include determining, after acquiring thefirst set of images and/or the second set of images, how many parametervalues are in the first set of parameter values and whether acorresponding set of images (e.g., with one image acquired at eachdifferent parameter value of the second set of parameter values) hasbeen acquired for each possible parameter value of the first set ofparameter values. If not, for example if additional images are to beacquired to complete the parameter selection acquisition protocol wherean image is acquired at each different possible combination of parametervalues, method 500 proceeds to 512 to acquire one or more respectivesets of images (and/or sets of cine loops) for each remaining parametervalue of the first set of parameter values, and then method 500 proceedsback to 510 to determine if a set of images has been acquired for eachparameter value of the first set of parameter values (e.g., if theacquisition protocol is complete).

If it is determined that a set of images has been acquired for eachparameter value of the first set of parameter values (e.g., that theacquisition protocol is complete), method 500 proceeds to 514 todetermine a quality metric for each image of each set of images (e.g.,each image acquired according to the parameter selection acquisitionprotocol) (and/or for each cine loop). The quality metric may bedetermined from a plurality of models, as indicated at 516. For example,as explained previously, a first quality metric may be determined by adepth model, such as depth model 209 of FIG. 2. Each image may beentered as an input to the depth model, and the depth model may output,for each input image, a respective first quality metric. In someexamples, the model may be selected based on the target scan plane. Forexample, a first depth model may be selected when the target scan planeis a four-chamber view and a second depth model may be selected when thetarget scan plane is a two-chamber view. Further, a second qualitymetric may be determined one or more frequency-related models, such asthe one or more frequency models 211 of FIG. 2. For example, each imagemay be entered as an input to a speckle model, a landmark detectionmodel, and/or a global image quality model, and the models may output,for each input image, a respective sub-metric. The respectivesub-metrics may be combined (e.g., added or averaged) to generate thesecond quality metric. In some examples, the model(s) may be selectedbased on the target scan plane. For example, a first landmark model maybe selected when the target scan plane is a four-chamber view and asecond landmark model may be selected when the target scan plane is atwo-chamber view. The first metric and the second metric for a givenimage may be combined to arrive at the quality metric for that image.

At 518, the image having the highest quality metric is selected. Forexample, referring back to FIG. 4, second image 404 was assigned animage metric (e.g., overall score) of 9, which is the highest qualitymetric of all the images acquired according to the acquisition protocoldescribed above with respect to FIG. 4. Thus, second image 404 may beselected. In examples where more than one image has the highest qualitymetric, the image that was acquired at the highest frequency may beselected. For example, in the example presented in FIG. 4, if secondimage 414 had a quality metric of 9 (e.g., rather than 8.9 as shown),second image 414 may be selected rather than second image 404 due tosecond image 414 being acquired at 1.7 MHz rather than 1.4 MHz.

At 520, shown in FIG. 5B, a first parameter value for the first scanparameter at which the selected image was acquired is identified and setas the selected parameter value of the first scan parameter. Forexample, if the first scan parameter is depth and the selected image wasacquired at a depth of 17 cm, the depth value of 17 cm may be identifiedas the selected parameter value. At 522, a second parameter value forthe second scan parameter at which the selected image was acquired isidentified and set as the selected parameter value of the second scanparameter. For example, if the second scan parameter is frequency andthe selected image was acquired at a frequency of 1.4 MHz, the frequencyvalue of 1.4 MHz may be identified as the selected parameter value. Insome examples, the selection of both the first parameter value and thesecond parameter value may only be performed once all images dictated bythe parameter selection acquisition protocol have been acquired.

At 523, method 500 optionally includes setting target post-acquisitionprocessing parameters, which is explained in more detail below withrespect to FIG. 8. Briefly, once the acquisition scan parameter valueshave been selected as explained above, target values for one or morepost-acquisition processing parameters may be selected based on theimage quality of one or more sets of adjusted images. For example, animage may be replicated, with different post-acquisition processingparameter values applied to each replicate, to create a set of adjustedimages. The image from the adjusted set of images that has the highestimage quality may be selected, and the parameter value(s) for that imagemay be applied to subsequent images. Example post-acquisition processingparameters include filtering parameters (e.g., bandwidth, centerfrequency), image contrast, image gain, etc.

At 524, one or more ultrasound images are acquired at the selected valuefor the first scan parameter and the selected value for the second scanparameter. Thus, once the scan parameter values have been selected forthe target scan plane based on the determined image quality metric asdescribed above, the selected scan parameter values may be set and anyadditional images acquired by the ultrasound probe may be acquired atthe set, selected scan parameter values. This may include setting thetransmit depth of the ultrasound probe to the selected depth value andsetting the transmit frequency of the ultrasound probe to the selectedfrequency. In some examples, the selected scan parameter values for thetarget scan plane may be saved in memory. Then, if the operator movesthe ultrasound probe so that the target scan plane is not imaged, butthen later moves the ultrasound probe back so that the target scan planeis imaged again, the previously determined selected scan parametervalues for that scan plane may be automatically applied. Additionally,if target post-acquisition processing parameters are set (e.g.,according to the method of FIG. 8), the one or more ultrasound imagesthat are acquired at 524 may be processed according to the targetpost-acquisition processing parameters, e.g., the received ultrasounddata may be filtered according to parameter values for the filtering(e.g., bandwidth and center frequency) that are determined according tothe method of FIG. 8.

At 526, method 500 determines if the current exam includes more targetplanes. The determination of whether the current exam includes moretarget planes may be made on the basis of user input. For example, theoperator may enter a user input indicating that a new scan plane isbeing imaged, that a new scan plane is about to be imaged, or that theexam is over. In other examples, the determination of whether the examincludes more target planes may be made automatically based on thesystem determining a different scan plane is being imaged or thatscanning has been terminated. If the exam does not include more targetscan planes, for example if the current exam is complete and imaging isterminated, method 500 proceeds to 528 to display acquired images,quality metrics, and selected parameter settings, and then method 500returns. It is to be understood that acquired images may be displayed at524 and/or other points during method 500. Further, the selectedparameter settings may be displayed at 524 to allow the operator to viewand confirm the parameter settings. The quality metrics may also bedisplayed at other points in time, such as at 524. Further, the imagesacquired at 524 may be archived when requested by the operator.

If the exam does include more target scan planes, method 500 proceeds to530 to determine if an indication that the next target plane is beingimaged has been received, similar to the determination made at 504 andexplained above. If the indication has not been received, method 500proceeds to 532 to continue to acquire images at the selected values forthe first and second scan parameters (e.g., as explained above withrespect to 524), and then method 500 returns to 530 to continue todetermine if the indication has been received. If the indication hasbeen received, method 500 proceeds to 534 and optionally restricts theparameter values for one or both of the first and second scanparameters. For example, as explained above, the first scan parametermay have three possible parameter values and the second scan parametermay have four possible scan values. However, once the selected parametervalues have been determined for a given scan plane, those selectedparameter values may be applied to the next target plane, thusrestricting the available values that may be optimized. For example, ifthe first target plane was a four-chamber view, and the next targetplane is a two-chamber view, one or both of the selected values may beused to acquire images in the two-chamber view. If one of the selectedvalues is used but not the other (e.g., the selected depth is used), theselection of the selected value for the other scan parameter (e.g.,frequency) may be re-performed for the next target plane. However, whenswitching from the four-chamber view to the PLAX view, for example, bothparameter values may be re-determined and thus 534 may not be performed.

At 536, 505-524 may be repeated for the next target plane. For example,a plurality of images may be acquired of the next target scan plane,each at a different combination of parameter values for the first scanparameter and second scan parameter, a quality metric may be determinedfor each image, and an image may be identified that has the highestquality metric. The acquisition settings used to acquire the selectedimage (e.g., the depth and frequency values) may be set as the selectedvalues for the first and second scan parameters, and one or moreadditional images of the next target scan plane may then be acquiredwith the selected values for the first and second scan parameters. Thisprocess may be repeated for all additional target scan planes, until theexam is complete.

While method 500 was described above with regard to varying depth andfrequency jointly to determine target depth and frequency values thatwill result in a high quality image, other acquisition scan parametersmay be varied according to the method described above without departingfrom the scope of this disclosure. For example, beamforming strategy andfrequency may be varied jointly. Beamforming strategy may include thetype of beamforming which is employed, e.g., the strength/type of ACEprocessing. Example beamforming strategies (which may be considered thedifferent “parameter values” for the beamforming strategy) may includedelay sum, coherent plane wave compounding, and divergent beam. Toselect a target beamforming strategy and frequency, a set of images maybe acquired, each at a different combination of beamforming strategy andfrequency (e.g., a first image at a first beamforming strategy and afirst frequency, a second image at a second beamforming strategy and thefirst frequency, a third image at the first beamforming strategy and asecond frequency, a fourth image at the second beamforming strategy andthe second frequency, and so forth). Each image of the set of images maybe assigned a quality metric, as described above. For example, eachimage may be input to the speckle model, the landmark detection model,and/or the global image quality model, and the models may output, foreach input image, a respective sub-metric. The respective sub-metricsmay be combined (e.g., added or averaged) to generate the quality metricfor each image. The image having the highest quality metric may beselected, and the beamforming strategy and frequency used to acquire theselected image may be set for subsequent image acquisition. In exampleswhere depth is not a scan parameter to be varied and selected, the depthmodel explained above may be omitted from the quality metricdetermination.

FIG. 6 shows a table 600 of different combinations of scan parametervalues (e.g., depth and frequency) that may be applied to acquire 12images, such as the 12 images shown in FIG. 4. Table 600 is an exampleof an acquisition protocol, which may dictate the order in which theimages are acquired (e.g., the order of which combinations ofacquisition parameter settings will be used). The ultrasound system mayhave a frame rate of 10 Hz, and thus an image may be acquired once everytenth of a second. As appreciated from table 600, the images may beacquired such that a first set of images is acquired with the same,first depth (from a set of three depths) and a different frequency (froma set of four frequencies), a second set of images is acquired with thesame, second depth (from the set of three depths) and a differentfrequency (from the set of four frequencies), and a third set of imagesis acquired with the same, third depth (from the set of three depths)and a different frequency (from the set of four frequencies). In thisway, for the first four images, depth may held constant while frequencyis varied. For the next four images, depth is held constant (at adifferent depth than the first four images) and frequency is varied, andfor the last four images, depth is held constant at a different depthand the frequency is varied.

As appreciated by table 600, an image will be acquired for each possiblecombination of parameter values for a first set of parameter valueshaving three values and a second set of parameter values having fourvalues. Table 600 may be stored in memory of a computing device (e.g.,memory 120 of FIG. 1) and, during parameter selection as described abovewith respect to FIGS. 5A and 5B, the images may be acquired according totable 600.

Because motion of the imaged anatomical features may contribute tofluctuations in image quality, it may be desirable to obtain the imagesdescribed herein (e.g., used to determine the optimal scan parameters)during periods where motion is not occurring, or during periods wheremotion among the images is comparable. When imaging the heart, obtainingimages with no motion or comparable motion may be challenging, given themovement of the heart over the course of a cardiac cycle. For example,for a patient having a heart rate of 60 beats per minute, a cardiaccycle may last one second, which is approximately the same amount oftime used to acquire all 12 images according to the table of FIG. 6.Thus, the decision of whether frequency is held constant for a durationwhile depth is varied (as shown by FIG. 4), or whether depth is heldconstant while frequency is varied (as shown by FIG. 6) may depend onwhat anatomy is being imaged (e.g., whether the heart is being imaged,and if so, which view of the heart).

FIG. 7 shows two example plots of ultrasound frequency as a function oftime during a parameter selection acquisition protocol where a pluralityof images are acquired. Each plot in FIG. 7 also shows an approximationof a cardiac cycle time aligned with the frequency. First plot 702illustrates frequency as a function of time relative to the cardiaccycle when, for each frequency, the frequency is held constant whiledepth is varied. This arrangement of scan parameter variability duringacquisition may result in all images having the first frequency (f1)being obtained during a first phase of the cardiac cycle (e.g. beginningof systole), all images having the second frequency (f2) being obtainedduring a second phase of the cardiac cycle (e.g., end of systole), allimages having the third frequency (f3) being obtained during a thirdphase of the cardiac cycle (e.g., beginning of diastole), and all imageshaving the fourth frequency (f4) being obtained during a fourth phase ofthe cardiac cycle (e.g., end of diastole).

In contrast, second plot 704 illustrates frequency as a function of timerelative to the cardiac cycle when, for each depth, the depth is heldconstant while frequency is varied. This arrangement of scan parametervariability during acquisition may result in an image having frequencyf1 being obtained at each cardiac phase (e.g., one during beginning ofsystole, one during the end of systole, one during the beginning ofdiastole, and one during the end of diastole. The remaining frequenciesmay follow a similar distribution (e.g., one image, for each frequency,at each phase of the cardiac cycle). The distribution shown in plot 704may be advantageous relative to the distribution shown by plot 702 whenmotion affects the frequency-based image quality detection to a higherdegree than the depth-based image quality detection. When images ofdifferent frequency but the same depth are compared to one another todetermine which image has the highest image quality, a more reliabledetermination may be made when all the images being compared areacquired in the same relative phase of the cardiac cycle. For example,as shown in plot 704, the first four frames of each image are eachacquired at a different frequency, but occur during the same phase ofthe cardiac cycle.

Turning now to FIG. 8, a method 800 for determining post-acquisitionprocessing parameters for ultrasound images is presented. Method 800 maybe carried out as part of method 500, for example once acquisitionparameter values for a target plane have been determined. In otherexamples, method 800 may be carried out independently of method 500, forexample in response to a user request to set post-acquisition processingparameter values. Method 800 may be carried out according toinstructions stored in non-transitory memory of a computing device, suchas image processing system 202 of FIG. 2.

At 802, ultrasound information for a single image is obtained. Theultrasound information may be acquired with an ultrasound probe inresponse to execution of method 800, or the ultrasound information maybe retrieved from memory. In one non-limiting example, the ultrasoundinformation may be ultrasound information sufficient to generate oneimage, and the ultrasound information may be obtained with targetacquisition scan parameters as discussed above (e.g., at a target depth,a target frequency, etc.).

At 804, different parameter values for a first post-acquisitionparameter are applied to the obtained ultrasound information to generatea first set of adjusted images. For example, the first post-acquisitionparameter may be a filtering center frequency, and the differentparameter values may be different center frequencies (e.g., 3.2 MHz, 3.4MHz, and 3.6 MHz, or different multiples of the transmission frequency,such as the transmission frequency, twice the transmission frequency,and three times the transmission frequency). In another example, thefirst post-acquisition parameter may be a filtering bandwidth and thedifferent parameter values may be different bandwidths (e.g., 1 MHz, 1.2MHz, and 1.4 MHz). Each different parameter value may be applied to theinformation to generate an image for each parameter value. For example,when the first post-acquisition parameter is the filtering centerfrequency, the first set of adjusted images may include a first imagegenerated with a center frequency of 3.2 MHz, a second image generatedwith a center frequency of 3.4 MHz, and a third image generated with acenter frequency of 3.6 MHz. The same ultrasound information may be usedto generate each image in the first set of adjusted images. Any otherpost-acquisition parameters may be held constant at a default orcommanded value.

At 806, a quality metric of each image in the first set of adjustedimages is determined. The quality metric of each image may be determinedby entering each image as input to one or more image quality models, asexplained above with respect to FIGS. 2, 3, 5A, and 5B. For example,each image may be entered as input to a global image quality model, alandmark detection model, and a speckle model, and each model may outputa respective quality sub-metric that may be summed or averaged to arriveat an overall image quality metric for each image.

At 808, the image of the first set of adjusted images having the highestimage quality metric is selected. If two or more images have the same,highest image quality metric, an additional metric may be used to selectfrom among the two or more images, such as the global image qualitymodel sub-metric. At 810, the first post-acquisition parameter is set tothe parameter value of the selected image. For example, if the selectedimage was generated with a filter center frequency of 3.2 MHz, thefilter center frequency may be set at 3.2 MHz.

At 812, the above process may be repeated for any additionalpost-acquisition parameters. For example, after selecting the firstpost-acquisition parameter value, the ultrasound information may againbe used to generate replicate images, with each replicate image having adifferent parameter value for a second post-acquisition parameters, suchas filter bandwidth, to form a second set of adjusted images. When thefirst post-acquisition parameter has been set, the images of the secondset of adjusted images may be generated with the set parameter value forthe first post-acquisition parameter. The image quality metric may bedetermined for each image in the second set of adjusted images, and theimage having the highest image quality metric may be selected. Theparameter value for the second post-acquisition parameter of the imagehaving the highest image quality metric may be set as the parametervalue for the second post-acquisition parameter. At 814, the setparameter value for each post-acquisition parameter is applied to anysubsequent images, e.g., of the current view plane. Method 800 thenends.

While method 800 was described above as including a sequential processfor selecting parameter values for two or more post-acquisitionparameters, a joint process may be used instead. In the joint process,one set of replicate images may be generated, where each image has adifferent combination of parameter values for the two or morepost-acquisition parameters. The image quality of each image may bedetermined as described above, and the image having the highest imagequality metric may be selected. The parameter values for the two or morepost-acquisition parameters used to generate the selected image may beselected and set as the parameter values for subsequent imageprocessing.

The image acquisition process used to acquire the ultrasound imagesdescribed herein may be carried out according to a suitable scansequence. FIG. 9 shows a graph 900 illustrating a first example scansequence that may be executed to acquire ultrasound information that maybe used to generate a single image. Graph 900 shows a sector scan,though other scan geometries are also possible. Each line in graph 900represents a transmit direction, and the transmits are firedsequentially from, for example, left to right. When a transmit parameteris varied, such as frequency, the transmits may all be fired at the sameparameter value (e.g., at the same frequency, P1) to generate a firstimage. Then, the frequency may be adjusted to a second frequency, andthe transmits may all be fired sequentially at the second frequency togenerate a second image.

The scan sequence of FIG. 9 would result in subsequent images beingobtained with different parameter values, when exploring for the targetparameter value. However, subsequent shots within an image are bepossible to be acquired with different parameters. This scan process foracquisition would include firing in the same transmit direction N timesto record data for that direction with N different parameter valuesbefore moving on to the next transmit direction. This scan process isshown in FIG. 10, which shows a graph 1000 illustrating a second examplescan sequence that may be executed to acquire ultrasound informationthat may be used to generate multiple images. Graph 1000 shows a sectorscan, though other scan geometries are also possible. Each solid line ingraph 1000 represents a transmit direction and a first parameter value,while each dashed line represents a different parameter value (fired atthe transmit direction of the solid line to its left) and the transmitsare fired sequentially from, for example, left to right. Instead ofincluding only one transmit parameter value, graph 1000 includes fourtransmit parameter values, P1-P4. For example, the transmit parametermay be frequency and each parameter value may be a different frequency.

During image acquisition, the transmits may be fired sequentially, butfor each transmit direction, a transmit may be fired for each parametervalue before moving on to the next transmit direction. For example, fora first transmit direction, a transmit may be fired at P1, a transmitmay be fired at P2, a transmit may be fired at P3, and a transmit may befired at P4 (while the different solid/dashed lines are placed besideseach other for illustration purposes, it is to be understood that eachtransmit for P1-P4 for the first transmit direction would be fired atthe same transmit direction). The transmit direction may be updated to asecond transmit direction, and a set of transmits may be fired at thesecond transmit direction, one for each parameter value. The process mayrepeat until all transmit directions have been fired at all parametervalues. A first image may be generated from information acquired whilefiring at the first parameter value, a second image may be generatedfrom information acquired while firing at the second parameter value, athird image may be generated from information acquired while firing atthe third parameter value, and a fourth image may be generated frominformation acquired while firing at the fourth parameter value. Thisscan sequence for parameter exploration would fire several times in eachdirection before moving on to the next transmit direction. This mayresult in longer time to acquire all directions of an image, but wouldresult in very low lag between the different parameters that are to becompared for image quality.

The scan sequence shown in FIG. 10 may be executed during the imageacquisition for parameter selection as described above with respect toFIGS. 5A and 5B, at least in some examples. In other examples, the scansequence shown in FIG. 9 may be executed during the image acquisitionfor parameter selection.

A technical effect of jointly selecting scan parameter values includesincreased image quality and reduced operator workflow demands. Anothertechnical effect is more consistent image quality across multiple exams.

In another representation, a system includes an ultrasound probe, amemory storing instructions, and a processor communicably coupled to thememory and when executing the instructions, configured to: processultrasound information obtained with the ultrasound probe into a firstset of replicate images, each replicate image processed according to adifferent post-acquisition processing parameter value of a plurality ofpost-acquisition processing parameter values for a firstpost-acquisition processing parameter; determine an image quality metricof each replicate image of the first set of replicate images; select thereplicate image having the highest image quality metric; and processadditionally acquired ultrasound information according to thepost-acquisition processing parameter value used to process theultrasound information into the selected replicate image. In an example,each replicate image of the first set of replicate images is processedfrom the same ultrasound information, such that the replicate images areidentical other than the different post-acquisition processing parametervalues used to create the replicate images. In an example, the processoris configured to, after selecting the replicate image having the highestimage quality metric: process the ultrasound information into a secondset of replicate images, each replicate image of the second set ofreplicate images processed according to a different post-acquisitionprocessing parameter value of a plurality of post-acquisition processingparameter values for a second post-acquisition processing parameter;determine an image quality metric of each replicate image of the secondset of replicate images; select the replicate image of the second set ofreplicate images having the highest image quality metric; and processadditionally acquired ultrasound information according to thepost-acquisition processing parameter value for the secondpost-acquisition processing parameter used to process the ultrasoundinformation into the selected replicate image. In an example, eachreplicate image of the first set of replicate images is processedaccording to a different parameter value for a second post-acquisitionprocessing parameter, and the additionally acquired ultrasoundinformation is processed according to the parameter value for the secondpost-acquisition processing parameter used to process the ultrasoundinformation into the selected replicate image. In an example, theultrasound information may be acquired with the ultrasound probe at afirst target scan parameter value and a second target scan parametervalue selected according to the joint process described above withrespect to FIGS. 5A and 5B.

When introducing elements of various embodiments of the presentdisclosure, the articles “a,” “an,” and “the” are intended to mean thatthere are one or more of the elements. The terms “first,” “second,” andthe like, do not denote any order, quantity, or importance, but ratherare used to distinguish one element from another. The terms“comprising,” “including,” and “having” are intended to be inclusive andmean that there may be additional elements other than the listedelements. As the terms “connected to,” “coupled to,” etc. are usedherein, one object (e.g., a material, element, structure, member, etc.)can be connected to or coupled to another object regardless of whetherthe one object is directly connected or coupled to the other object orwhether there are one or more intervening objects between the one objectand the other object. In addition, it should be understood thatreferences to “one embodiment” or “an embodiment” of the presentdisclosure are not intended to be interpreted as excluding the existenceof additional embodiments that also incorporate the recited features.

In addition to any previously indicated modification, numerous othervariations and alternative arrangements may be devised by those skilledin the art without departing from the spirit and scope of thisdescription, and appended claims are intended to cover suchmodifications and arrangements. Thus, while the information has beendescribed above with particularity and detail in connection with what ispresently deemed to be the most practical and preferred aspects, it willbe apparent to those of ordinary skill in the art that numerousmodifications, including, but not limited to, form, function, manner ofoperation and use may be made without departing from the principles andconcepts set forth herein. Also, as used herein, the examples andembodiments, in all respects, are meant to be illustrative only andshould not be construed to be limiting in any manner.

1. A method, comprising: acquiring a plurality of ultrasound images ofan anatomical region, each ultrasound image acquired at a differentcombination of parameter values for a first scan parameter and a secondscan parameter; selecting a first parameter value for the first scanparameter and a second parameter value for the second scan parameterbased on an image quality of each image; and acquiring one or moreadditional ultrasound images at the first parameter value and the secondparameter value.
 2. The method of claim 1, wherein the parameter valuescomprise a first set of parameter values for the first scan parameterand a second set of parameter values for the second scan parameter, andwherein acquiring the plurality of images comprises acquiring a firstset of images each at a different parameter value of the first set ofparameter values and the same, first parameter value of the second setof parameter values, and acquiring a second set of images each at adifferent parameter value of the first set of parameter values and thesame, second parameter value of the second set of parameter values. 3.The method of claim 2, wherein acquiring the plurality of images furthercomprises acquiring one or more additional sets of images, each image ofeach respective additional set of images acquired at a differentparameter value of the first set of parameter values and the same,respective subsequent parameter value of the second set of parametervalues.
 4. The method of claim 1, wherein selecting the first parametervalue comprises determining a respective image quality metric for eachimage using a plurality of models, selecting an image of the pluralityof images that has a highest image quality metric, and setting the firstparameter value to the parameter value of the first scan parameter atwhich the selected image was acquired.
 5. The method of claim 4, whereinselecting the first parameter value comprises setting the secondparameter value to the parameter value of the second scan parameter atwhich the selected image was acquired.
 6. The method of claim 1, whereinthe first scan parameter comprises depth and the second scan parametercomprises frequency.
 7. The method of claim 1, wherein the first scanparameter comprises frequency and the second scan parameter comprisesbeamforming strategy.
 8. A method for an ultrasound system, comprising:responsive to a determination that a target scan plane of an anatomicalregion is currently being imaged with the ultrasound system, jointlyselecting a target value for a first scan parameter and a target valuefor a second scan parameter based on a respective image quality metricfor each of a plurality of images of the anatomical region in the targetscan plane; and applying the selected target value for the first scanparameter and the selected target value for the second scan parameter toone or more additional images of the anatomical region.
 9. The method ofclaim 8, wherein jointly selecting the target value for the first scanparameter and the target value for the second scan parameter comprisesacquiring each image of the plurality of images at a differentcombination of values for the first scan parameter and values for thesecond scan parameter, each value for the first scan parameter selectedfrom a set of values for the first scan parameter and each value for thesecond scan parameter selected from a set of values for the second scanparameter, and wherein applying the selected target value for the firstscan parameter and the selected target value for the second scanparameter to one or more additional images of the anatomical regioncomprises acquiring the one or more additional images of the anatomicalregion at the selected target value for the first scan parameter and theselected target value for the second scan parameter.
 10. The method ofclaim 9, wherein acquiring each image of the plurality of images at adifferent combination of values for the first scan parameter and valuesfor the second scan parameter comprises: acquiring a set of firstimages, each first image acquired at the same, first value for the firstscan parameter and at a different value for the second scan parameter,and acquiring a set of second images, each second image acquired at thesame, second value for the first scan parameter and at a different valuefor the second scan parameter.
 11. The method of claim 10, whereinjointly selecting the target value for the first scan parameter and thetarget value for the second scan parameter based on the respective imagequality metric for each of the plurality of images further comprises:selecting an image from the plurality of images that has a highest imagequality metric, setting the target value for the first scan parameter asthe value for the first scan parameter at which the selected image wasacquired, and setting the target value for the second scan parameter asthe value for the second scan parameter at which the selected image wasacquired.
 12. The method of claim 11, wherein determining the respectiveimage quality metric for each image comprises determining a respectiveimage quality metric for each image via one or more image qualitymodels.
 13. The method of claim 12, wherein determining the respectiveimage quality metric for each image via one or more image quality modelscomprises, for each image: determining a first sub-metric via a globalimage quality model; determining a second sub-metric via a landmarkmodel; determining a third sub-metric via a speckle size model; andgenerating the image quality metric for that image by summing the firstsub-metric, the second sub-metric, and the third sub-metric.
 14. Themethod of claim 8, wherein the first scan parameter comprisesbeamforming strategy and the second scan parameter comprises frequency.15. The method of claim 8, wherein the first scan parameter is a firstpost-acquisition processing parameter and the second scan parameter is asecond post-acquisition processing parameter, wherein the method furthercomprises generating the plurality of images by processing ultrasoundinformation to generate a set of replicate images, each replicate imageof the set of replicate images processed according to a differentcombination of parameter values for the first post-acquisitionprocessing parameter and the second post-acquisition processingparameter; and wherein applying the selected target value for the firstscan parameter and the selected target value for the second scanparameter to one or more additional images of the anatomical regioncomprises processing subsequently acquired ultrasound image informationaccording to the selected target value for the first post-acquisitionscan parameter and the selected target value for the secondpost-acquisition processing parameter.
 16. The method of claim 8,wherein the first scan parameter comprises depth and the second scanparameter comprises frequency, wherein the target scan plane is a firsttarget scan plane, the target value for the first scan parameter is afirst depth value, the target value for the second scan parameter is afirst frequency value, and further comprising responsive to adetermination that a second target scan plane of the anatomical regionis currently being imaged with the ultrasound system: selecting a seconddepth value and a second frequency value based on a respective imagequality metric for each of a second plurality of sequentially acquiredimages of the anatomical region in the second target scan plane and/orbased on the first depth value and the first frequency value; andacquiring one or more additional images of the anatomical region at thesecond depth value and the second frequency value.
 17. A system,comprising: an ultrasound probe; a memory storing instructions; and aprocessor communicably coupled to the memory and when executing theinstructions, configured to: responsive to a determination that a targetscan plane of an anatomical region is currently being imaged with theultrasound probe, jointly select a target value for a first scanparameter and a target value for a second scan parameter based on arespective image quality metric for each of a plurality of images of theanatomical region in the target scan plane; and apply the selectedtarget value for the first scan parameter and the selected target valuefor the second scan parameter to one or more additional images of theanatomical region.
 18. The system of claim 17, wherein the memory storesone or more neural networks, and wherein when executing theinstructions, the processor is configured to input each image to the oneor more neural networks to determine the respective image quality metricof each image.
 19. The system of claim 17, wherein the first scanparameter comprises beamforming strategy or depth and the second scanparameter comprises frequency.
 20. The system of claim 17, wherein thefirst scan parameter comprises a post-acquisition filtering bandwidthand the second scan parameter comprises a post-acquisition filteringcentering frequency.